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Electrophysiological recording and analysis during protracted continuous work experiments
Computer Methods and Programs in Biomedicine, 39 (1993) 323-332
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Elsevier Science Publishers B.V. COMMET 01339 Section II. Systems and programs
Electrophysiological recording and analysis during protracted continuous work experiments R . A . P i g e a u ~, M. G r u s h c o w b R . E . H u g h e s a, R.J. H e s l e g r a v e a and R.G. Angus ~ a Defence and Civil Institute of Environmental Medicine North York, Ontario, and h NTT Systems Inc., Don Mills, Ontario, Canada
A system is described that allows electrophysiological data to be collected and analysed from sleep loss studies involving continuous cognitive work. This system needed to satisfy two major requirements. First, the electrophysiological hardware had to be able to record extended periods of physiology(often lasting 80 h) from multiple electrode sites and yet be portable enough to allow subject mobility. Second, a comprehensive yet flexible procedure had to be developed to temporally map the physiologyto critical experimental events (e.g., all the behavioural tasks, sleeping and napping periods). The resulting system uses the Oxford Medilog 9000 for data acquisition and playback as well as a custom software environment to digitize and analyse these data. Sleep loss; Electrophysiology;Cognitive performance
1. Introduction
Sleep loss studies have repeatedly demonstrated that sleep deprivation is associated with higher levels of (subjective) fatigue and sleepiness as well as poorer cognitive performance [1-4]. However, such studies are often characterized by a paucity of behavioural tasks administered relatively infrequently. Also, the tasks are typically not cognitively demanding and may be separated by long periods of leisure activity (e.g., reading or watching movies). For these reasons Angus and Heslegrave [5] suggested that performance declines due to sleep loss reported in many studies may be conservative and not reflect performance impairment expected in actual work environments. Their work showed that when sleep depri-
Correspondence: R.A. Pigeau, Defence and Civil Institute of Environmental Medicine, P.O. Box 2000, North York, Ontario, Canada M3M 3B9. Tel.: (416) 635-2045.
vation is coupled with intensive cognitive work, performance declines of 3 0 - 4 0 % are found after the first night and 6 0 - 7 0 % declines are observed following the second night without sleep [5,6]. The logistics required to conduct experiments requiring prolonged work periods are considerable. Presentation and timing of behavioural tasks, data logging of subjects' responses, multisubject monitoring by the experimenters, food preparation, and general experimental control must be carefully orchestrated. In our laboratory much of this work has been simplified by automating the experimental delivery system. All tasks are computer generated, timed, coordinated and presented. A multi-day agenda is pre-programmed such that each minute of the study is under experimenter control. During such experiments numerous computer files are generated for each subject, cataloguing their responses to each stimulus for each task throughout the entire experiment. In this environment, logistical problems are
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compounded when electrophysiological recordings are added. Monitoring autonomic, central and somatic nervous system processes (e.g., EEG, ECG, EOG, body temperature, respiration) under continuous work conditions offers substantial scientific benefits--e.g., from examining the electrophysiological correlates of declining mental performance to monitoring sleep recovery processes. However, adding an electrophysiological dimension to data acquisition requires a complex experimental environment (including hardware, software and organization). It is necessary to (1) continuously record electrophysiology from several subjects with multiple leads, (2) correlate these signals with each of the many behavioural tasks performed throughout the experiment, and (3) retrieve, filter and process the electrophysiology post hoc. The present paper describes a solution to the problem of continuous electrophysiological recording during prolonged (cognitive) work experiments. When developing this system the philosophy has been to maximize experimental flexibility and facilitate data collection, retrieval and analysis.
2. System description 2.1. Experimental environment A complete description of our experimental environment can be found in [5,6]. For illustration purposes what follows is a brief outline of a typical 5-day experiment. Four subjects are run concurrently in a selfcontained experimental facility. On their arrival they are briefed on the experiment and for the remainder of the first day are given extensive training and practice on the battery of cognitive tasks to be used in the experiment. Prior to the 8 hours of baseline sleep allowed the first night, subjects are fitted with electrophysiological recording equipment which is worn and kept operational throughout the entire experiment. At 06:00 on the second day the subjects are awakened and begin a 64-h period of continuous cognitive work. During this time the subjects are
deprived of sleep and must work continuously in 1.75-h work sessions with 15-min breaks devoted to experimental and subject related needs--e.g., checking the electrodes, eating, using the lavatory, etc. The entire protocol is automated so that all tasks are presented individually to each subject on a display terminal. On the evening of the fourth day the subjects are allowed 8 h of recovery sleep after which they are awakened and tested for a final 6 h. 2.2. Hardware In order to adequately assess electrophysiological effects of continuous work, the hardware had to meet the following specific requirements: able to compactly record electrophysiology from multiple electrodes for long periods and yet also allow subject motility. The Oxford Instruments Medilog 9000 T M was chosen as the ambulatory electrophysiological recording system. It consists of two parts: a Medilog MR-90 portable recorder and a Medilog 9000 Replay and Display system. Although the entire system was originally developed for clinical rather than experimental use, this system met the stated requirements. The Oxford MR-90 is a small portable recorder capable of storing 8 channels of physiology on a standard (low noise) 120 rain audio cassette for a period of approximately 24 h. The recorder stores all 8 channels as well as real-time information once every second by generating (and recording on a separate channel) a 128-Hz square wave that effectively functions as a 256-Hz trigger signal to sample the incoming data. These data are stored on tape using blocked analogue technology. A manually activated event marker is included which, if pressed, is integrated onto the timing information. The MR-90 can be configured to record EEG, ECG, EMG, EOG, respiration, temperature and body movement. It has a real time clock which functions for about 5 months on 2 watch batteries, and can continuously record physiology for 24 h using 4 alkaline 'AA' batteries. It is self-contained, lightweight (700 g) and can be easily worn. Data recorded on the MR-90 can be played back using the Oxford Medilog 9000 display sys-
325 tern which reconstructs the signals into an analog format. Among its many features are replay speeds of 1-, 20-, 40- or 60-times 'real time'. All replayed channels are sent both to the unit's CRT for display and to an output port in analog form allowing A / D conversions. A GPIB interface is also included to remotely control all playback and display functions. Although the Medilog 9000 satisfied our two most important research requirements (i.e., portability and extended recordings), it has limitations. First, the input modules, each designed for specific physiological signals (e.g., EEG, ECG, etc.), offer little flexibility. For example, the E E G modules are more suited to multiple bipolar recordings rather than a monopolar montage (i.e., using a c o m m o n reference). Second, the recorder's event marker cannot be operated remotely. This can be a deficiency because the subject must consciously press the event marker to tag critical experimental e v e n t s - - a cumbersome procedure if the number of events is large. Third, tape speed accuracy is limited to 0.1% which can cause large errors when analysing over long periods. Fourth, the system has a rather narrow frequency response from DC to 35 Hz, due largely to the multiplexing necessary to condense 8 channels of data onto a standard 4-track audio cassette. Strategies for dealing with some of these limitations are described in section 2.4. A DEC V A X / 7 8 5 operating under VMS with A / D converters and mass storage was used to run the software. A minicomputer this powerful is not necessary to analyse electrophysiological data, however, it was used to run the original experiment and was available.
2.3. Software During the continuous work experiment described in section 2.1 three types of data are produced: (1) a trace file containing the times when each task was started for each subject, (2) data files containing timed behavioural responses for each subject on all tasks, and (3) continuous recordings of electrophysiology on cassette tapes (in this case 5 cassettes for each subject with a single cassette holding approximately 18 h of
data). The problem is to locate the physiological data recorded on tape for each subject corresponding to the period during which any particular trial of a task was performed. Our software embodies three major components: (1) a syntax checker that inspects and compiles preparatory files needed to define and control analysis requests; (2) a menu-driven interface that guides the user through numerous options and procedures; and (3) a control process that actually performs the analyses. The control process itself governs four subprocesses that implement data acquisition, extraction, analysis and report generation utilities. The overall modularity of the software facilitates error checking as well as simplifying the addition of new features (e.g., other signal processing algorithms or analysis options).
2.3.1. Files needed to perform analyses Suppose one wishes to analyse a subject's physiology when performing all instances of the Subtraction, Logical Reasoning and Serial Reaction Time tasks during an entire experiment. A method is needed to specify, in chronological order, the start times that these tasks were performed and to determine which cassette tapes contain the relevant physiological data. It is also necessary to specify certain analysis parameters for each task, such as the size and number of epochs to digitize. Given the length of our experiments and the number of tasks performed, a multitude of possible analysis requests is available. Therefore, software was written to construct an Analysis Control File that contains all of the information required to perform the requisite analyses. To create this control file four other files must first be present: (1) the experiment trace file, (2) the subject tape file, (3) the analysis parameter file, and (4) the analysis request file. These are described below.
Experiment Trace File. Throughout the experiment task start times are recorded when they are presented to the subjects. A single trace file containing this information for all subjects is automatically created during each experiment.
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Subject Tape File. The experiment
trace file does not contain information about when the Medilog cassette tapes were changed for each subject. Upon completion of the experiment it is necessary to manually create a short file, one for each subject, containing the dates and times when the physiology was recorded on each cassette tape. An example of a subject tape file is the following: 20-AUG-90 21-AUG-90 22-AUG-90 23-AUG-90 23-AUG-90
21:35:30 48-l 17:45:27 48-2 11:50:57 48-3 05:47:03 48-4 2212013348-5
In this example starting dates, times and tape labels are listed for cassettes 1 through 5 from subject no. 48. Analysis Parameter File. Depending
(1)
EXPERIMENTS 212,213 task LRT id 7 ida 2.4.5 np 4 ip’30 trials I, 3-X,10-19.21-32 Ids 2,4,h
“p IO lp 60 trials 2,9,20,33 task SER id 10 ids 3,4,5 “p 2 lp 60 trials l-70 task SUB id X Ids 4.4,s
“p 2 Ip 60 trlals 1,3-X,10-19.21-32 np IO Ip 60 rrials 2,9,20,33
Fig. 1. This example of an Analysis Parameter File would be interpreted as follows: (1) the Analysis Parameter File is appropriate for experiments numbered 212 and 213; (2) the Logical Reasoning Task (LRT) is to be given the identification tag of 7 in all result files created by the analyses-i.e., a data field in the results files is given the pre-assigned value 7 to identify LRT; (3) other identification tags can also be included in the result files-to identify within subject independent variables used in the experiments such as trials ocurring before and after naps; (4) perform electrophysiological analyses on each of 4 epochs (np = no. of epochs) lasting 30 s (Ip = length of epochs); (5) the trials of the Logical Reasoning Task for which these parameters apply; (6) other identification tags-e.g., lo-min versions of the task; (7) perform electrophysiological analyses on each of ten 60-s epochs; (8) the trials of the Logical Reasoning Task for which these new parameters apply; (9) and so on for the trials of the remaining tasks.
on the experiment, trials for some tasks may be longer than others. For example, there may be short and long versions of the Logical Reasoning, Serial Reaction Time and the Subtraction tasks (e.g., 2 vs. 10 min). Also, a few of these tasks may have been placed at critical periods during an experiment (e.g., the first few test sessions after sleep periods -for estimating sleep inertia). A method is needed to indentify these instances, tag them and specify unique analysis parameters. Usually one (complete) Analysis Parameter File needs to be (manually) created at the end of an experiment. However, multiple files are allowed if one wishes to consider alternative analysis strategies. An example of an Analysis Parameter File can be seen in Fig. 1. It contains parameter information that is common to two separate experiments and includes identifiers, epoch length and number of epochs for each trial of each task.
Analysis Request File contains instructions from the experimenter directing the software to analyse specific tasks. These instructions are formulated using a high level command language (written for this purpose) which can be independent of both the subjects and the experiment. For example, the simple request
Analysis Request File. Contained
Analyse all 5 minute trials of LRT
within the Experiment Trace, Subject Tape and Analysis Parameter files is all the information necessary to trace chronologically each subject’s (task driven) activity throughout the experiment as well as the parameters required to parcel and identify the electrophysiological data. What remains is a method for specifying particular analyses. The
can be applied to all subjects from any experiment in which the Logical Reasoning Task was performed. The language is very flexible and forgiving; it is not sensitive to case, contains many options and the keywords may be truncated (as long as they do not become ambiguous). Fig. 2
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(1) (2) (3) (4) (5)
Analyze LRT trials 1 2 3 analyze SUB from tape 1 10:00 to tape 2 13:(X) anaLyze lrt trials 5-40 of duration 100-120 seconds analyze SER with ids 3 4 5 using ids 1 2 3 ANALYse all tasks from SUB trial l to tape 5 11:35:24
Fig. 2. This example of an Analysis Request File would be interpreted as follows: (1) analyse the Logical Reasoning Task for trials 1, 2, 3; (2) analyse the Subtraction Task for any trials which start between tape 1 at 10:00 and tape 2 at 13:/)0; (3) analyse the Logical Reasoning Task for any trials from 5 through 40 that have a duration of 100-120 s; (4) analyse the Serial Reaction Time Task for identification values of 3, 4 and 5 and change these identification values to I, 2, and 3; (5) analyse all tasks between the first trial of the Subtraction Task and the time 11:35:24 on the fifth tape.
illustrates a single Analysis Request File containing many requests.
2.3.2. Menu interface To perform electrophysiological data acquisition and analysis one must prepare and compile various files, manipulate cassette tapes containing the electrophysiological data, and run the Medilog playback unit. The commands that implement these procedures along with the many analysis options are highly parameterized and thus difficult to remember. A menu oriented interface was written in VAX DCL (Digital Command Language) to guide the user. Menu choices select the required programs and create command line options, switches and parameters. Throughout the session the interface verifies that the necessary files exist and that access to needed directories is allowed. Also, useful default values are provided which, if changed, are remembered in order to facilitate multiple analysis requests. The main menu contains the following analysis options (see Fig. 3): (1) (2) (3) (4) (5) (6)
compile analysis parameter file; create subject trace file; compile analysis request; analyse calibration signal; collect and save digitized data; perform E E G / E C G analyses.
Typically, the first two menu options are invoked only once per experiment. They check for syntax errors in the subject trace and analysis parameter files and convert them into a format suitable for the analysis request compiler. The third option invokes this compiler which assimilates the information from the preparatory files (see section 2.3.1) and creates a subject specific Analysis Control File. The Analysis Control File contains all of the information necessary for analysing the electrophysiological data: for example, each trial of each (requested) task has a start date and time, duration, cassette tape identification number, epoch length and number of epochs. The Analysis Control File is sorted in chronological order and must be created before analyses can be performed. The last three menu options perform the analyses and are described below. It should be stressed, however, that the fourth and fifth menu options ('Analyse calibration signal' and 'Collect and save digitized data') are included as separate items solely for the convenience of the experimenters. Both routines can be invoked from within the sixth menu option to facilitate the entire analysis process.
Analyse calibration signal. Each cassette of electrophysiological data contains 20 rain of calibration signals for each channel. The calibration signal is a 5 Hz sine wave that is either 100 /.~V (peak to peak) for E E G and EOG, 25 /.~V for E M G or 1 mV for ECG. The software directs the user to locate the begining of the calibration signal and start the playback unit. The calibration signal from the chosen channel is digitized, and weighting factors are produced which are used to calculate accurate microvolt or millivolt values during signal processing. Collect and saL,e digitized data. Due to the large number of tasks performed in our experiments for which electrophysiology was obtained, it was considered too demanding for the Oxford replay hardware to use the GPIB interface. Numerous mechanical searches would be involved to continually locate appropriate sections of tape (i.e., performing multiple 'fast forwards', 'stops', 'play'
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and 'rewinds' to find specific start times). Since disk space was not a problem on our computer system, the decision was made to digitize the entire time between the first and last tasks (for a given tape) found in the Analysis Control File. Often 18 h of tape would be digitized and saved in a single file. Instructions are given to insert the correct tape and position it at the correct start time. When data digitization begins an estimated completion time is given which will vary as a function of the tape playback speed (i.e., 20-, 40-, 60-times 'realtime').
Perform EEG /ECG analyses. This menu item offers the experimenter a host of options to facilitate and control EEG and ECG analyses. The software queries the experimenter for the names of the Analysis Control File and the file of digi-
tized data. If the data have not been previously digitized there is a facility to invoke directly the data collection routines within this menu option. This is often the preferred strategy when multiple tapes are involved because analyses can be performed concurrently with the collection process (see section 2.3.3). Also, there exists an option to analyse physiological data without first having an Analysis Control File. This is advantageous for studying physiological changes during periods when tasks were not performed--e.g., naps, baseline and recovery sleep periods. In such instances the software will query the experimenter to provide essential information (e.g., start time, number of epochs, epoch length, etc.) and automatically create a short Analysis Control File. The collection routines will then be invoked to calibrate and digitize the data before analyses are performed.
I AnalysisReques(~ File • (Subject TraceFile]~ ........
~ [ ~ ) MenuOption [ - - " ~ DiskFile
~ompiled Analysi~ [,. ParameterFile
( File of Digitized"~
Listof Symbols Fig. 3. The Menu Interface and the files necessary to collect and analyse the electrophysiological data.
329 2.3.3. Control process The software discussed in the previous two sections offers a powerful and flexible interface for the experimenter to initiate analyses. Transparent to the user is software responsible for sequencing all data analysis activities. This 'control process (see Fig. 4) implements and coordinates subprocesses for (1) digitizing and storing electrophysiological data from the cassette tapes; (2) extracting the portions of digitized data defined by the user's analysis request; (3) performing signal processing; and (4) formatting the resuits in an output file for subsequent statistical analysis (using commercial statistical packages, e.g., SAS, BMDP). The control process and the subprocesses are written in the C programming language.
ili::~
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i~::~::i::~
.:g~".~!~::~
The data acquisition process is responsible for digitizing and storing analogue data from the. Medilog 9000 playback unit. It operates independently of, and asynchronously with, the other three processes. It receives digitizing commands from the control process, one for each tape contained in the analysis request file. The amount of data to be digitized from each tape is bound by the start time of the first task and the end time of the last task of interest. It is often the case that a significant portion of the digitized data will not be analysed--e.g., electrophysiology associated with breaks or answering questionnaires. Digitized data from each tape is stored in a separate file with a file header that identifies the start time, duration, channel number and sampling rate (currently fixed at 256 Hz, see section 2.4).
~i~ii::i::iii:. ===================================
:!~::::i::i::;::::
f-----q [] Process Software ([~
Menu Option List of Symbols
Fig. 4. The Control process and the concurrentlyrunning subprocesses performingthe analyses.
330 Once the first tape has been digitized, the control process uses the remaining subrocesses to analyze the data corresponding to the tasks listed in the analysis request file. With each task defined by its start and end times, the extraction process determines where the appropriate data is located in the digitized file. The extraction process then reads the digitized data and copies it into a memory buffer shared by it and the analysis process. As the analysis process analyses the data, the extraction process copies more from the file into the buffer. This continues until all of the data associated with a task have been read. As the analysis process reads and analyses the data, it produces intermediate result records which are written to another memory buffer shared with the report generation process. The report generation process reads the results in the buffer (as they are being written) and produces a final output file suitable for further statistical analysis (using commercially available software). All subprocesses concurrently send status reports to the control process, allowing it to coordinate the entire activity. The exercise is repeated by the control process for the next task listed in the analysis request file.
2. 4. Signal processing Due to the hardware chosen to collect and playback the physiological data, as well as the strategy we have taken to retrieve these data, signal processing problems were encountered. To locate the correct segments of physiological data in our large digitized files requires knowledge of both the sampling frequency and the tasks' start times. The relevant temporal positions can then be calculated using this information. Unfortunately this strategy requires an extremely stable playback system. Any variation in tape speed during playback would introduce large compounding errors. By sampling the Medilog's timing channel we found that significant tape drift occurred after a relatively short time; following 18 h of digitization, timing could be incorrect by as much as 10 min. To solve this problem, the 128-Hz square wave signal generated and recorded on the tapes by the Medilog MR-90 is
used to activate a Schmitt trigger on the A / D converters. Since both the negative and positive inflections of the 128-Hz signal activate the trigger, an effective 256-Hz sampling rate is produced. The 128-Hz signal varies isomorphically with tape playback speed and generates digitized data temporally equivalent to the original signal. In fact, testing revealed that after 18 h of digitization a difference of only 1 s is detected between actual tape time and the time estimated from the sampling frequency. Unfortunately, using the Schmitt trigger in our A / D hardware limits digitization to one channel, so multi-channel sampling has been sacrificed.
2.5. Data analysis A number of analyses have been incorporated in the software and can be applied to any digitized signal (where appropriate). These include period analysis, fast Fourier transforms (FFTs) and a number of ECG feature detection algorithms. The period analysis produces three measures (zero-cross, power, and first-derivative [7]) for each of as many as 10 definable EEG bandwidths. FFTs can be performed sequentially on any size temporal window. Also, FFTs can be calculated on overlapping windows which allows temporal smoothing of the frequency domain and can be used to observe relatively fast transitions in the EEG (e.g., [8]). ECG analyses produce both interbeat-intervals as well as amplitude and duration measures from key waveform features.
3. Status report
The system is currently used to analyse electrophysiological data in our laboratory. Pigeau et al. [8,9] investigated the relationship between fatigue and mental performance during sleep deprivation. One task, performed hourly by each subject, instructs them to relax in their seat with eyes closed. After 4 min, a bell signals them to open their eyes and rate the level of drowsiness they experienced. The EEG for each of these 4-min
331 --
Subjective Estimate of Drowsiness EEG Defined Drowsiness
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Fig. 5. Changes in two measures of drowsiness taken from a 4-rain eyes-closed relaxation period occurring once every hour throughout the experiment. One is a subjective estimate based on a questionnaire given at the end of the task. The other, derived from period analysed EEG, is calculated using the formula:
A-b-
(Tb+Db)
+l
where A, T and D equal the alpha, theta and delta frequency bands, and Ab, Tb and Db equal baseline levels of alpha, theta and delta--i.e., those obtained at the beginning of the experiment. The data are averaged over 9 subjects.
periods was analysed using our electrophysiological analysis system. For any given subject at least 60 such 4-min periods occurred in our experiment. The 4 min were divided into 24 10-s epochs upon which period analysis was performed to extract percent and power estimates for each of five E E G frequency bandwidths (delta 0.5-4 Hz, theta 4 - 8 Hz, alpha 8-12 Hz, sigma 12-16 Hz and beta 16-40 Hz). Fig. 5 shows changes throughout the experiment in self-reported levels of drowsiness as well as drowsiness defined quantitatively from the EEG. The curves in Fig. 5 are highly correlated (r = 0.82). A similar correlation ( r = 0.84) is found if the E E G estimates of drowsiness (from the 4-min eyes closed periods) are plotted against performance on the Logical Reasoning Task [10]. The system is designed such that it is not restricted to laboratory experiments. The Medilog units are portable and can be taken into the field.
Also, our experimental environment can be run on any computer with the VMS operating system. Recently [11], data were collected using this system in an investigation of thermal stress and high workload on CF 118 (Hornet) pilots flying multiple daily missions while wearing heavy protective clothing. The programs and Medilog hardware were transported to an operational air base where the software was installed on a VAX computer running a VMS operating system. Behavioural tasks were presented to the pilots on a VT100 terminal and electrophysiological data were collected continuously. Our system was used to analyse core body temperature and heart rate under various experimental conditions (including flying, sleeping and performing cognitive tasks). Although the system's current implementation has some limitations--e.g., poor clock synchronization, no on-line capability--modifications are under way to address these restrictions.
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References [1] D.R. Haslam, Sleep loss, recovery sleep and military performance, Ergonomics 5 (1982) 163-178. [2] L.C. Johnson and P. Naitoh, The operational consequences of sleep deprivation and sleep deficit (NATO Advisory Group for Aerospace Research and Development, AGARDograph No. 193, Paris, 1974). [3] P. Naitoh, Circadian cycles and restorative power of naps, in Biological Rhythms, Sleep and Shift Work, eds. L.C. Johnson, D.I. Tepas, W.P. Colquohoun and M.J. Colligan, pp. 553-585 (Spectrum, New York, NY, 1982). [4] P.D. Opstad, M. Ekanger, M. Nummestrad and N. Raabe, Performance, mood and clinical symptoms in men exposed to prolonged, severe physical work and sleep deprivation, Aviat. Space Environ. Med. 49 (1978) 10651073. [5] R. Angus and R. Heslegrave, Effects of sleep loss on sustained cognitive performance during a command and control simulation, Behav. Res. Methods Instrum. Comput. 17 (1985) 55-67. [6] R.J. Heslegrave and R.G. Angus, The effects of task duration and worksession location on performance degradation induced by sleep loss and sustained cognitive work, Behav. Res. Methods Instrum. Comput. 17 (1985) 592-603. [7] R.F. Hoffmann, A.R. Moffitt, J.C. Shearer, P.S. Sussmann and R. Wells, Conceptual and methodological con-
[8]
[9]
[10]
[11]
siderations towards the development of computer-controlled research on the electrophysiology of sleep, Waking Sleeping 3 (1979) 1-16. R.A. Pigeau, R.G. Angus and R.J. Heslegrave, Electrophysiological measures of mental fatigue and declining performance resulting from sleep loss, in Proceedings of the 29th Military Testing Association Conference pp. 584-589 (Ottawa, Canada, 1987). R.A. Pigeau, R.J. Heslegrave and R.G. Angus, Psychophysiological measures of drowsiness as estimators of mental fatigue and performance degradation during sleep deprivation, in AGARD Conference Proceedings No. 432 'Electric and Magnetic Activity of the Central Nervous System: Research and Clinical Applications in Aerospace Medicine', pp. 1-16 (Trondheim, Norway, 1988). R.G. Angus, R.J. Heslegrave and R.A. Pigeau, Sustained operations studies: from the field to the laboratory, in Proceedings of the Commission of the European Communities Medical and Public Health Research Programme Workshop on 'Polyphasic and Ultrashort SleepWake Patterns--Biological, Methodological and Medical Aspects: Relationship to Performance in Sustained Operations' (Castello di Gargonza, Italy, 1988). R.J. Heslegrave, J. Frim, L.L. Bossi and J.R. Popplow, The psychological, physiological, and performance impact of sustained NBC operations on CF-18 Fighter Pilots, (DCIEM Report, Defence and Civil Institute of Environmental Medicine, No. 90-08, 1990).
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Elsevier Science Publishers B.V. COMMET 01339 Section II. Systems and programs
Electrophysiological recording and analysis during protracted continuous work experiments R . A . P i g e a u ~, M. G r u s h c o w b R . E . H u g h e s a, R.J. H e s l e g r a v e a and R.G. Angus ~ a Defence and Civil Institute of Environmental Medicine North York, Ontario, and h NTT Systems Inc., Don Mills, Ontario, Canada
A system is described that allows electrophysiological data to be collected and analysed from sleep loss studies involving continuous cognitive work. This system needed to satisfy two major requirements. First, the electrophysiological hardware had to be able to record extended periods of physiology(often lasting 80 h) from multiple electrode sites and yet be portable enough to allow subject mobility. Second, a comprehensive yet flexible procedure had to be developed to temporally map the physiologyto critical experimental events (e.g., all the behavioural tasks, sleeping and napping periods). The resulting system uses the Oxford Medilog 9000 for data acquisition and playback as well as a custom software environment to digitize and analyse these data. Sleep loss; Electrophysiology;Cognitive performance
1. Introduction
Sleep loss studies have repeatedly demonstrated that sleep deprivation is associated with higher levels of (subjective) fatigue and sleepiness as well as poorer cognitive performance [1-4]. However, such studies are often characterized by a paucity of behavioural tasks administered relatively infrequently. Also, the tasks are typically not cognitively demanding and may be separated by long periods of leisure activity (e.g., reading or watching movies). For these reasons Angus and Heslegrave [5] suggested that performance declines due to sleep loss reported in many studies may be conservative and not reflect performance impairment expected in actual work environments. Their work showed that when sleep depri-
Correspondence: R.A. Pigeau, Defence and Civil Institute of Environmental Medicine, P.O. Box 2000, North York, Ontario, Canada M3M 3B9. Tel.: (416) 635-2045.
vation is coupled with intensive cognitive work, performance declines of 3 0 - 4 0 % are found after the first night and 6 0 - 7 0 % declines are observed following the second night without sleep [5,6]. The logistics required to conduct experiments requiring prolonged work periods are considerable. Presentation and timing of behavioural tasks, data logging of subjects' responses, multisubject monitoring by the experimenters, food preparation, and general experimental control must be carefully orchestrated. In our laboratory much of this work has been simplified by automating the experimental delivery system. All tasks are computer generated, timed, coordinated and presented. A multi-day agenda is pre-programmed such that each minute of the study is under experimenter control. During such experiments numerous computer files are generated for each subject, cataloguing their responses to each stimulus for each task throughout the entire experiment. In this environment, logistical problems are
324
compounded when electrophysiological recordings are added. Monitoring autonomic, central and somatic nervous system processes (e.g., EEG, ECG, EOG, body temperature, respiration) under continuous work conditions offers substantial scientific benefits--e.g., from examining the electrophysiological correlates of declining mental performance to monitoring sleep recovery processes. However, adding an electrophysiological dimension to data acquisition requires a complex experimental environment (including hardware, software and organization). It is necessary to (1) continuously record electrophysiology from several subjects with multiple leads, (2) correlate these signals with each of the many behavioural tasks performed throughout the experiment, and (3) retrieve, filter and process the electrophysiology post hoc. The present paper describes a solution to the problem of continuous electrophysiological recording during prolonged (cognitive) work experiments. When developing this system the philosophy has been to maximize experimental flexibility and facilitate data collection, retrieval and analysis.
2. System description 2.1. Experimental environment A complete description of our experimental environment can be found in [5,6]. For illustration purposes what follows is a brief outline of a typical 5-day experiment. Four subjects are run concurrently in a selfcontained experimental facility. On their arrival they are briefed on the experiment and for the remainder of the first day are given extensive training and practice on the battery of cognitive tasks to be used in the experiment. Prior to the 8 hours of baseline sleep allowed the first night, subjects are fitted with electrophysiological recording equipment which is worn and kept operational throughout the entire experiment. At 06:00 on the second day the subjects are awakened and begin a 64-h period of continuous cognitive work. During this time the subjects are
deprived of sleep and must work continuously in 1.75-h work sessions with 15-min breaks devoted to experimental and subject related needs--e.g., checking the electrodes, eating, using the lavatory, etc. The entire protocol is automated so that all tasks are presented individually to each subject on a display terminal. On the evening of the fourth day the subjects are allowed 8 h of recovery sleep after which they are awakened and tested for a final 6 h. 2.2. Hardware In order to adequately assess electrophysiological effects of continuous work, the hardware had to meet the following specific requirements: able to compactly record electrophysiology from multiple electrodes for long periods and yet also allow subject motility. The Oxford Instruments Medilog 9000 T M was chosen as the ambulatory electrophysiological recording system. It consists of two parts: a Medilog MR-90 portable recorder and a Medilog 9000 Replay and Display system. Although the entire system was originally developed for clinical rather than experimental use, this system met the stated requirements. The Oxford MR-90 is a small portable recorder capable of storing 8 channels of physiology on a standard (low noise) 120 rain audio cassette for a period of approximately 24 h. The recorder stores all 8 channels as well as real-time information once every second by generating (and recording on a separate channel) a 128-Hz square wave that effectively functions as a 256-Hz trigger signal to sample the incoming data. These data are stored on tape using blocked analogue technology. A manually activated event marker is included which, if pressed, is integrated onto the timing information. The MR-90 can be configured to record EEG, ECG, EMG, EOG, respiration, temperature and body movement. It has a real time clock which functions for about 5 months on 2 watch batteries, and can continuously record physiology for 24 h using 4 alkaline 'AA' batteries. It is self-contained, lightweight (700 g) and can be easily worn. Data recorded on the MR-90 can be played back using the Oxford Medilog 9000 display sys-
325 tern which reconstructs the signals into an analog format. Among its many features are replay speeds of 1-, 20-, 40- or 60-times 'real time'. All replayed channels are sent both to the unit's CRT for display and to an output port in analog form allowing A / D conversions. A GPIB interface is also included to remotely control all playback and display functions. Although the Medilog 9000 satisfied our two most important research requirements (i.e., portability and extended recordings), it has limitations. First, the input modules, each designed for specific physiological signals (e.g., EEG, ECG, etc.), offer little flexibility. For example, the E E G modules are more suited to multiple bipolar recordings rather than a monopolar montage (i.e., using a c o m m o n reference). Second, the recorder's event marker cannot be operated remotely. This can be a deficiency because the subject must consciously press the event marker to tag critical experimental e v e n t s - - a cumbersome procedure if the number of events is large. Third, tape speed accuracy is limited to 0.1% which can cause large errors when analysing over long periods. Fourth, the system has a rather narrow frequency response from DC to 35 Hz, due largely to the multiplexing necessary to condense 8 channels of data onto a standard 4-track audio cassette. Strategies for dealing with some of these limitations are described in section 2.4. A DEC V A X / 7 8 5 operating under VMS with A / D converters and mass storage was used to run the software. A minicomputer this powerful is not necessary to analyse electrophysiological data, however, it was used to run the original experiment and was available.
2.3. Software During the continuous work experiment described in section 2.1 three types of data are produced: (1) a trace file containing the times when each task was started for each subject, (2) data files containing timed behavioural responses for each subject on all tasks, and (3) continuous recordings of electrophysiology on cassette tapes (in this case 5 cassettes for each subject with a single cassette holding approximately 18 h of
data). The problem is to locate the physiological data recorded on tape for each subject corresponding to the period during which any particular trial of a task was performed. Our software embodies three major components: (1) a syntax checker that inspects and compiles preparatory files needed to define and control analysis requests; (2) a menu-driven interface that guides the user through numerous options and procedures; and (3) a control process that actually performs the analyses. The control process itself governs four subprocesses that implement data acquisition, extraction, analysis and report generation utilities. The overall modularity of the software facilitates error checking as well as simplifying the addition of new features (e.g., other signal processing algorithms or analysis options).
2.3.1. Files needed to perform analyses Suppose one wishes to analyse a subject's physiology when performing all instances of the Subtraction, Logical Reasoning and Serial Reaction Time tasks during an entire experiment. A method is needed to specify, in chronological order, the start times that these tasks were performed and to determine which cassette tapes contain the relevant physiological data. It is also necessary to specify certain analysis parameters for each task, such as the size and number of epochs to digitize. Given the length of our experiments and the number of tasks performed, a multitude of possible analysis requests is available. Therefore, software was written to construct an Analysis Control File that contains all of the information required to perform the requisite analyses. To create this control file four other files must first be present: (1) the experiment trace file, (2) the subject tape file, (3) the analysis parameter file, and (4) the analysis request file. These are described below.
Experiment Trace File. Throughout the experiment task start times are recorded when they are presented to the subjects. A single trace file containing this information for all subjects is automatically created during each experiment.
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Subject Tape File. The experiment
trace file does not contain information about when the Medilog cassette tapes were changed for each subject. Upon completion of the experiment it is necessary to manually create a short file, one for each subject, containing the dates and times when the physiology was recorded on each cassette tape. An example of a subject tape file is the following: 20-AUG-90 21-AUG-90 22-AUG-90 23-AUG-90 23-AUG-90
21:35:30 48-l 17:45:27 48-2 11:50:57 48-3 05:47:03 48-4 2212013348-5
In this example starting dates, times and tape labels are listed for cassettes 1 through 5 from subject no. 48. Analysis Parameter File. Depending
(1)
EXPERIMENTS 212,213 task LRT id 7 ida 2.4.5 np 4 ip’30 trials I, 3-X,10-19.21-32 Ids 2,4,h
“p IO lp 60 trials 2,9,20,33 task SER id 10 ids 3,4,5 “p 2 lp 60 trials l-70 task SUB id X Ids 4.4,s
“p 2 Ip 60 trlals 1,3-X,10-19.21-32 np IO Ip 60 rrials 2,9,20,33
Fig. 1. This example of an Analysis Parameter File would be interpreted as follows: (1) the Analysis Parameter File is appropriate for experiments numbered 212 and 213; (2) the Logical Reasoning Task (LRT) is to be given the identification tag of 7 in all result files created by the analyses-i.e., a data field in the results files is given the pre-assigned value 7 to identify LRT; (3) other identification tags can also be included in the result files-to identify within subject independent variables used in the experiments such as trials ocurring before and after naps; (4) perform electrophysiological analyses on each of 4 epochs (np = no. of epochs) lasting 30 s (Ip = length of epochs); (5) the trials of the Logical Reasoning Task for which these parameters apply; (6) other identification tags-e.g., lo-min versions of the task; (7) perform electrophysiological analyses on each of ten 60-s epochs; (8) the trials of the Logical Reasoning Task for which these new parameters apply; (9) and so on for the trials of the remaining tasks.
on the experiment, trials for some tasks may be longer than others. For example, there may be short and long versions of the Logical Reasoning, Serial Reaction Time and the Subtraction tasks (e.g., 2 vs. 10 min). Also, a few of these tasks may have been placed at critical periods during an experiment (e.g., the first few test sessions after sleep periods -for estimating sleep inertia). A method is needed to indentify these instances, tag them and specify unique analysis parameters. Usually one (complete) Analysis Parameter File needs to be (manually) created at the end of an experiment. However, multiple files are allowed if one wishes to consider alternative analysis strategies. An example of an Analysis Parameter File can be seen in Fig. 1. It contains parameter information that is common to two separate experiments and includes identifiers, epoch length and number of epochs for each trial of each task.
Analysis Request File contains instructions from the experimenter directing the software to analyse specific tasks. These instructions are formulated using a high level command language (written for this purpose) which can be independent of both the subjects and the experiment. For example, the simple request
Analysis Request File. Contained
Analyse all 5 minute trials of LRT
within the Experiment Trace, Subject Tape and Analysis Parameter files is all the information necessary to trace chronologically each subject’s (task driven) activity throughout the experiment as well as the parameters required to parcel and identify the electrophysiological data. What remains is a method for specifying particular analyses. The
can be applied to all subjects from any experiment in which the Logical Reasoning Task was performed. The language is very flexible and forgiving; it is not sensitive to case, contains many options and the keywords may be truncated (as long as they do not become ambiguous). Fig. 2
327
(1) (2) (3) (4) (5)
Analyze LRT trials 1 2 3 analyze SUB from tape 1 10:00 to tape 2 13:(X) anaLyze lrt trials 5-40 of duration 100-120 seconds analyze SER with ids 3 4 5 using ids 1 2 3 ANALYse all tasks from SUB trial l to tape 5 11:35:24
Fig. 2. This example of an Analysis Request File would be interpreted as follows: (1) analyse the Logical Reasoning Task for trials 1, 2, 3; (2) analyse the Subtraction Task for any trials which start between tape 1 at 10:00 and tape 2 at 13:/)0; (3) analyse the Logical Reasoning Task for any trials from 5 through 40 that have a duration of 100-120 s; (4) analyse the Serial Reaction Time Task for identification values of 3, 4 and 5 and change these identification values to I, 2, and 3; (5) analyse all tasks between the first trial of the Subtraction Task and the time 11:35:24 on the fifth tape.
illustrates a single Analysis Request File containing many requests.
2.3.2. Menu interface To perform electrophysiological data acquisition and analysis one must prepare and compile various files, manipulate cassette tapes containing the electrophysiological data, and run the Medilog playback unit. The commands that implement these procedures along with the many analysis options are highly parameterized and thus difficult to remember. A menu oriented interface was written in VAX DCL (Digital Command Language) to guide the user. Menu choices select the required programs and create command line options, switches and parameters. Throughout the session the interface verifies that the necessary files exist and that access to needed directories is allowed. Also, useful default values are provided which, if changed, are remembered in order to facilitate multiple analysis requests. The main menu contains the following analysis options (see Fig. 3): (1) (2) (3) (4) (5) (6)
compile analysis parameter file; create subject trace file; compile analysis request; analyse calibration signal; collect and save digitized data; perform E E G / E C G analyses.
Typically, the first two menu options are invoked only once per experiment. They check for syntax errors in the subject trace and analysis parameter files and convert them into a format suitable for the analysis request compiler. The third option invokes this compiler which assimilates the information from the preparatory files (see section 2.3.1) and creates a subject specific Analysis Control File. The Analysis Control File contains all of the information necessary for analysing the electrophysiological data: for example, each trial of each (requested) task has a start date and time, duration, cassette tape identification number, epoch length and number of epochs. The Analysis Control File is sorted in chronological order and must be created before analyses can be performed. The last three menu options perform the analyses and are described below. It should be stressed, however, that the fourth and fifth menu options ('Analyse calibration signal' and 'Collect and save digitized data') are included as separate items solely for the convenience of the experimenters. Both routines can be invoked from within the sixth menu option to facilitate the entire analysis process.
Analyse calibration signal. Each cassette of electrophysiological data contains 20 rain of calibration signals for each channel. The calibration signal is a 5 Hz sine wave that is either 100 /.~V (peak to peak) for E E G and EOG, 25 /.~V for E M G or 1 mV for ECG. The software directs the user to locate the begining of the calibration signal and start the playback unit. The calibration signal from the chosen channel is digitized, and weighting factors are produced which are used to calculate accurate microvolt or millivolt values during signal processing. Collect and saL,e digitized data. Due to the large number of tasks performed in our experiments for which electrophysiology was obtained, it was considered too demanding for the Oxford replay hardware to use the GPIB interface. Numerous mechanical searches would be involved to continually locate appropriate sections of tape (i.e., performing multiple 'fast forwards', 'stops', 'play'
328
and 'rewinds' to find specific start times). Since disk space was not a problem on our computer system, the decision was made to digitize the entire time between the first and last tasks (for a given tape) found in the Analysis Control File. Often 18 h of tape would be digitized and saved in a single file. Instructions are given to insert the correct tape and position it at the correct start time. When data digitization begins an estimated completion time is given which will vary as a function of the tape playback speed (i.e., 20-, 40-, 60-times 'realtime').
Perform EEG /ECG analyses. This menu item offers the experimenter a host of options to facilitate and control EEG and ECG analyses. The software queries the experimenter for the names of the Analysis Control File and the file of digi-
tized data. If the data have not been previously digitized there is a facility to invoke directly the data collection routines within this menu option. This is often the preferred strategy when multiple tapes are involved because analyses can be performed concurrently with the collection process (see section 2.3.3). Also, there exists an option to analyse physiological data without first having an Analysis Control File. This is advantageous for studying physiological changes during periods when tasks were not performed--e.g., naps, baseline and recovery sleep periods. In such instances the software will query the experimenter to provide essential information (e.g., start time, number of epochs, epoch length, etc.) and automatically create a short Analysis Control File. The collection routines will then be invoked to calibrate and digitize the data before analyses are performed.
I AnalysisReques(~ File • (Subject TraceFile]~ ........
~ [ ~ ) MenuOption [ - - " ~ DiskFile
~ompiled Analysi~ [,. ParameterFile
( File of Digitized"~
Listof Symbols Fig. 3. The Menu Interface and the files necessary to collect and analyse the electrophysiological data.
329 2.3.3. Control process The software discussed in the previous two sections offers a powerful and flexible interface for the experimenter to initiate analyses. Transparent to the user is software responsible for sequencing all data analysis activities. This 'control process (see Fig. 4) implements and coordinates subprocesses for (1) digitizing and storing electrophysiological data from the cassette tapes; (2) extracting the portions of digitized data defined by the user's analysis request; (3) performing signal processing; and (4) formatting the resuits in an output file for subsequent statistical analysis (using commercial statistical packages, e.g., SAS, BMDP). The control process and the subprocesses are written in the C programming language.
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i~::~::i::~
.:g~".~!~::~
The data acquisition process is responsible for digitizing and storing analogue data from the. Medilog 9000 playback unit. It operates independently of, and asynchronously with, the other three processes. It receives digitizing commands from the control process, one for each tape contained in the analysis request file. The amount of data to be digitized from each tape is bound by the start time of the first task and the end time of the last task of interest. It is often the case that a significant portion of the digitized data will not be analysed--e.g., electrophysiology associated with breaks or answering questionnaires. Digitized data from each tape is stored in a separate file with a file header that identifies the start time, duration, channel number and sampling rate (currently fixed at 256 Hz, see section 2.4).
~i~ii::i::iii:. ===================================
:!~::::i::i::;::::
f-----q [] Process Software ([~
Menu Option List of Symbols
Fig. 4. The Control process and the concurrentlyrunning subprocesses performingthe analyses.
330 Once the first tape has been digitized, the control process uses the remaining subrocesses to analyze the data corresponding to the tasks listed in the analysis request file. With each task defined by its start and end times, the extraction process determines where the appropriate data is located in the digitized file. The extraction process then reads the digitized data and copies it into a memory buffer shared by it and the analysis process. As the analysis process analyses the data, the extraction process copies more from the file into the buffer. This continues until all of the data associated with a task have been read. As the analysis process reads and analyses the data, it produces intermediate result records which are written to another memory buffer shared with the report generation process. The report generation process reads the results in the buffer (as they are being written) and produces a final output file suitable for further statistical analysis (using commercially available software). All subprocesses concurrently send status reports to the control process, allowing it to coordinate the entire activity. The exercise is repeated by the control process for the next task listed in the analysis request file.
2. 4. Signal processing Due to the hardware chosen to collect and playback the physiological data, as well as the strategy we have taken to retrieve these data, signal processing problems were encountered. To locate the correct segments of physiological data in our large digitized files requires knowledge of both the sampling frequency and the tasks' start times. The relevant temporal positions can then be calculated using this information. Unfortunately this strategy requires an extremely stable playback system. Any variation in tape speed during playback would introduce large compounding errors. By sampling the Medilog's timing channel we found that significant tape drift occurred after a relatively short time; following 18 h of digitization, timing could be incorrect by as much as 10 min. To solve this problem, the 128-Hz square wave signal generated and recorded on the tapes by the Medilog MR-90 is
used to activate a Schmitt trigger on the A / D converters. Since both the negative and positive inflections of the 128-Hz signal activate the trigger, an effective 256-Hz sampling rate is produced. The 128-Hz signal varies isomorphically with tape playback speed and generates digitized data temporally equivalent to the original signal. In fact, testing revealed that after 18 h of digitization a difference of only 1 s is detected between actual tape time and the time estimated from the sampling frequency. Unfortunately, using the Schmitt trigger in our A / D hardware limits digitization to one channel, so multi-channel sampling has been sacrificed.
2.5. Data analysis A number of analyses have been incorporated in the software and can be applied to any digitized signal (where appropriate). These include period analysis, fast Fourier transforms (FFTs) and a number of ECG feature detection algorithms. The period analysis produces three measures (zero-cross, power, and first-derivative [7]) for each of as many as 10 definable EEG bandwidths. FFTs can be performed sequentially on any size temporal window. Also, FFTs can be calculated on overlapping windows which allows temporal smoothing of the frequency domain and can be used to observe relatively fast transitions in the EEG (e.g., [8]). ECG analyses produce both interbeat-intervals as well as amplitude and duration measures from key waveform features.
3. Status report
The system is currently used to analyse electrophysiological data in our laboratory. Pigeau et al. [8,9] investigated the relationship between fatigue and mental performance during sleep deprivation. One task, performed hourly by each subject, instructs them to relax in their seat with eyes closed. After 4 min, a bell signals them to open their eyes and rate the level of drowsiness they experienced. The EEG for each of these 4-min
331 --
Subjective Estimate of Drowsiness EEG Defined Drowsiness
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.5
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Fig. 5. Changes in two measures of drowsiness taken from a 4-rain eyes-closed relaxation period occurring once every hour throughout the experiment. One is a subjective estimate based on a questionnaire given at the end of the task. The other, derived from period analysed EEG, is calculated using the formula:
A-b-
(Tb+Db)
+l
where A, T and D equal the alpha, theta and delta frequency bands, and Ab, Tb and Db equal baseline levels of alpha, theta and delta--i.e., those obtained at the beginning of the experiment. The data are averaged over 9 subjects.
periods was analysed using our electrophysiological analysis system. For any given subject at least 60 such 4-min periods occurred in our experiment. The 4 min were divided into 24 10-s epochs upon which period analysis was performed to extract percent and power estimates for each of five E E G frequency bandwidths (delta 0.5-4 Hz, theta 4 - 8 Hz, alpha 8-12 Hz, sigma 12-16 Hz and beta 16-40 Hz). Fig. 5 shows changes throughout the experiment in self-reported levels of drowsiness as well as drowsiness defined quantitatively from the EEG. The curves in Fig. 5 are highly correlated (r = 0.82). A similar correlation ( r = 0.84) is found if the E E G estimates of drowsiness (from the 4-min eyes closed periods) are plotted against performance on the Logical Reasoning Task [10]. The system is designed such that it is not restricted to laboratory experiments. The Medilog units are portable and can be taken into the field.
Also, our experimental environment can be run on any computer with the VMS operating system. Recently [11], data were collected using this system in an investigation of thermal stress and high workload on CF 118 (Hornet) pilots flying multiple daily missions while wearing heavy protective clothing. The programs and Medilog hardware were transported to an operational air base where the software was installed on a VAX computer running a VMS operating system. Behavioural tasks were presented to the pilots on a VT100 terminal and electrophysiological data were collected continuously. Our system was used to analyse core body temperature and heart rate under various experimental conditions (including flying, sleeping and performing cognitive tasks). Although the system's current implementation has some limitations--e.g., poor clock synchronization, no on-line capability--modifications are under way to address these restrictions.
332
References [1] D.R. Haslam, Sleep loss, recovery sleep and military performance, Ergonomics 5 (1982) 163-178. [2] L.C. Johnson and P. Naitoh, The operational consequences of sleep deprivation and sleep deficit (NATO Advisory Group for Aerospace Research and Development, AGARDograph No. 193, Paris, 1974). [3] P. Naitoh, Circadian cycles and restorative power of naps, in Biological Rhythms, Sleep and Shift Work, eds. L.C. Johnson, D.I. Tepas, W.P. Colquohoun and M.J. Colligan, pp. 553-585 (Spectrum, New York, NY, 1982). [4] P.D. Opstad, M. Ekanger, M. Nummestrad and N. Raabe, Performance, mood and clinical symptoms in men exposed to prolonged, severe physical work and sleep deprivation, Aviat. Space Environ. Med. 49 (1978) 10651073. [5] R. Angus and R. Heslegrave, Effects of sleep loss on sustained cognitive performance during a command and control simulation, Behav. Res. Methods Instrum. Comput. 17 (1985) 55-67. [6] R.J. Heslegrave and R.G. Angus, The effects of task duration and worksession location on performance degradation induced by sleep loss and sustained cognitive work, Behav. Res. Methods Instrum. Comput. 17 (1985) 592-603. [7] R.F. Hoffmann, A.R. Moffitt, J.C. Shearer, P.S. Sussmann and R. Wells, Conceptual and methodological con-
[8]
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[11]
siderations towards the development of computer-controlled research on the electrophysiology of sleep, Waking Sleeping 3 (1979) 1-16. R.A. Pigeau, R.G. Angus and R.J. Heslegrave, Electrophysiological measures of mental fatigue and declining performance resulting from sleep loss, in Proceedings of the 29th Military Testing Association Conference pp. 584-589 (Ottawa, Canada, 1987). R.A. Pigeau, R.J. Heslegrave and R.G. Angus, Psychophysiological measures of drowsiness as estimators of mental fatigue and performance degradation during sleep deprivation, in AGARD Conference Proceedings No. 432 'Electric and Magnetic Activity of the Central Nervous System: Research and Clinical Applications in Aerospace Medicine', pp. 1-16 (Trondheim, Norway, 1988). R.G. Angus, R.J. Heslegrave and R.A. Pigeau, Sustained operations studies: from the field to the laboratory, in Proceedings of the Commission of the European Communities Medical and Public Health Research Programme Workshop on 'Polyphasic and Ultrashort SleepWake Patterns--Biological, Methodological and Medical Aspects: Relationship to Performance in Sustained Operations' (Castello di Gargonza, Italy, 1988). R.J. Heslegrave, J. Frim, L.L. Bossi and J.R. Popplow, The psychological, physiological, and performance impact of sustained NBC operations on CF-18 Fighter Pilots, (DCIEM Report, Defence and Civil Institute of Environmental Medicine, No. 90-08, 1990).